The present application relates to Traffic Scene Recognition in Monocular Videos.
In the US alone, road accidents cost over 32000 lives and $275 billion in economic losses annually. Improved advanced warning systems (AWS) in automobiles can alleviate some of these costs. While conventional collision avoidance systems can detect objects that pose a danger, the AWS window can be significantly expanded by a semantically meaningful recognition of traffic scenes. Visual scene recognition, thus, can play a significant role in predicting the possibility of danger in traffic videos. A few key challenges faced by scene recognition are the complexity of traffic scenarios where multiple scenes may occur simultaneously with several participants involved, the vast number of possible scene types and the need for online, real-time solutions. The efficacy of advance warning systems (AWS) in automobiles can be significantly enhanced by semantic recognition of traffic scenes that pose a potential danger. However, the complexity of road scenes and the need for real-time solutions pose key challenges.